Funny... I can't reproduce this on my machine. What happens if you explicitly `gc()` between the various scenarios (i.e. time multiplication by random, call `gc()`, then time multiplication by zeros)? What happens if you time multiplication by random a few times in a row? A few zeros in a row?
// T On Thursday, July 17, 2014 1:54:54 PM UTC+2, Andrei Zh wrote: > > I continue investigating matrix multiplication performance. Today I found > that multiplication by array of zeros(..) is several times faster than > multiplication by array of ones(..) or random numbers: > > julia> A = rand(200, 100) > ... > > julia> @time for i=1:1000 A * rand(100, 200) end > elapsed time: 3.009730414 seconds (480160000 bytes allocated, 11.21% gc > time) > > julia> @time for i=1:1000 A * ones(100, 200) end > elapsed time: 2.973320655 seconds (480128000 bytes allocated, 12.72% gc > time) > > julia> @time for i=1:1000 A * zeros(100, 200) end > elapsed time: 0.438900132 seconds (480128000 bytes allocated, 85.46% gc > time) > > So, A * zeros() is about 6 faster than other kinds of multiplication. Note > also that it uses ~7x more GC time. > > On NumPy no such difference is seen: > > In [106]: %timeit dot(A, rand(100, 200)) > 100 loops, best of 3: 2.77 ms per loop > > In [107]: %timeit dot(A, ones((100, 200))) > 100 loops, best of 3: 2.59 ms per loop > > In [108]: %timeit dot(A, zeros((100, 200))) > 100 loops, best of 3: 2.57 ms per loop > > > So I'm curious, how multiplying by zeros matrix is different from other > multiplication types? > > >
